Exploration of COVID-19 tracking data from multiple resources
Wei Sun
2020-04-14
1 Introduction
Coronavirus disease 2019 (COVID-19) is an infectious disease caused by a new type of coronavirus: severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak first started in Wuhan, China in December 2019. The first kown case of COVID-19 in the U.S. was confirmed on January 20, 2020, in a 35-year-old man who teturned to Washington State on January 15 after traveling to Wuhan. Starting around the end of Feburary, evidence emerge for community spread in the US.
We, as all of us, are indebted to the heros who fight COVID-19 across the whole world in different ways. For this data exploration, I am grateful to many data science groups who have collected detailed COVID-19 outbreak data, including the number of tests, confirmed cases, and deaths, across countries/regions, states/provnices (administrative division level 1, or admin1), and counties (admin2). Specifically, I used the data from these three resources:
JHU (https://coronavirus.jhu.edu/)
The Center for Systems Science and Engineering (CSSE) at John Hopkins University.
World-wide counts of coronavirus cases, deaths, and recovered ones.
NY Times (https://www.nytimes.com/interactive/2020/us/coronavirus-us-cases.html)
The New York Times
``cumulative counts of coronavirus cases in the United States, at the state and county level, over time’’
COVID Trackng (https://covidtracking.com/)
COVID Tracking Project
``collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data’’
2 JHU
Assume you have cloned the JHU Github repository on your local machine at ``../COVID-19’’.
2.1 time series data
The time series provide counts (e.g., confirmed cases, deaths) starting from Jan 22nd, 2020 for 253 locations. Currently there is no data of individual US state in these time series data files.
Here is the list of 10 records with the largest number of cases or deaths on the most recent date.
Next, I check for each country/region, what is the number of new cases/deaths? This data is important to understand what is the trend under different situations, e.g., population density, social distance policies etc. Here I checked the top 10 countries/regions with the highest number of deaths.
2.2 daily reports data
The raw data from Hopkins are in the format of daily reports with one file per day. More recent files (since March 22nd) inlcude information from individual states of US or individual counties, as shown in the following figure. So I turn to NY Times data for informatoin of individual states or counties.
3 NY Times
The data from NY Times are saved in two text files, one for state level information and the other one for county level information.
The currente date is
## [1] "2020-04-13"3.1 state level data
First check the 20 states with the largest number of deaths.
## date state fips cases deaths
## 2308 2020-04-13 New York 36 195031 10056
## 2306 2020-04-13 New Jersey 34 64584 2443
## 2298 2020-04-13 Michigan 26 25487 1601
## 2294 2020-04-13 Louisiana 22 21016 884
## 2297 2020-04-13 Massachusetts 25 26867 844
## 2289 2020-04-13 Illinois 17 22025 800
## 2279 2020-04-13 California 6 24334 725
## 2281 2020-04-13 Connecticut 9 13381 602
## 2315 2020-04-13 Pennsylvania 42 24295 563
## 2326 2020-04-13 Washington 53 10538 525
## 2284 2020-04-13 Florida 12 21011 498
## 2285 2020-04-13 Georgia 13 13125 479
## 2290 2020-04-13 Indiana 18 8236 350
## 2321 2020-04-13 Texas 48 14488 320
## 2280 2020-04-13 Colorado 8 7691 308
## 2312 2020-04-13 Ohio 39 6975 274
## 2296 2020-04-13 Maryland 24 8936 262
## 2328 2020-04-13 Wisconsin 55 3428 155
## 2325 2020-04-13 Virginia 51 5747 149
## 2301 2020-04-13 Missouri 29 4388 137For these 20 states, I check the number of new cases and the number of new deaths. Part of the reason for such checking is to identify whether there is any similarity on such patterns. For example, could you use the pattern seen from Italy to predict what happen in an individual state, and what are the similarities and differences across states.
Next I check the relation between the cumulative number of cases and deaths for these 10 states, starting on March
3.2 county level data
First check the 20 counties with the largest number of deaths.
## date county state fips cases deaths
## 55443 2020-04-13 New York City New York NA 106764 7154
## 55442 2020-04-13 Nassau New York 36059 24358 1109
## 55027 2020-04-13 Wayne Michigan 26163 11648 760
## 55470 2020-04-13 Westchester New York 36119 19785 610
## 55462 2020-04-13 Suffolk New York 36103 21643 580
## 54414 2020-04-13 Cook Illinois 17031 15474 543
## 55368 2020-04-13 Bergen New Jersey 34003 10092 482
## 55373 2020-04-13 Essex New Jersey 34013 7634 433
## 55008 2020-04-13 Oakland Michigan 26125 5073 347
## 54032 2020-04-13 Los Angeles California 6037 9420 320
## 56386 2020-04-13 King Washington 53033 4551 298
## 54125 2020-04-13 Fairfield Connecticut 9001 6004 262
## 54867 2020-04-13 Orleans Louisiana 22071 5651 244
## 54995 2020-04-13 Macomb Michigan 26099 3418 240
## 55375 2020-04-13 Hudson New Jersey 34017 7879 236
## 55386 2020-04-13 Union New Jersey 34039 6636 217
## 55378 2020-04-13 Middlesex New Jersey 34023 5987 204
## 54857 2020-04-13 Jefferson Louisiana 22051 5088 186
## 55454 2020-04-13 Rockland New York 36087 7965 182
## 54945 2020-04-13 Middlesex Massachusetts 25017 5983 163For these 20 counties, I check the number of new cases and the number of new deaths.
4 COVID Trackng
The positive rates of testing can be an indicator on how much the COVID-19 has spread. However, they are more noisy data since the negative testing resutls are often not reported and the tests are almost surely taken on a non-representative random sample of the population. The COVID traking project proides a grade per state: ``If you are calculating positive rates, it should only be with states that have an A grade. And be careful going back in time because almost all the states have changed their level of reporting at different times.’’ (https://covidtracking.com/about-tracker/). The data are also availalbe for both counties and states, here I only look at state level data.
Since the daily postive rate can fluctuate a lot, here I only illustrae the cumulative positave rate across time, for four states with grade A data. Of course since this is an R markdown file, you can modify the source code and check for other states.
5 Session information
## R version 3.6.2 (2019-12-12)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] httr_1.4.1 ggpubr_0.2.5 magrittr_1.5 ggplot2_3.2.1
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.3 pillar_1.4.3 compiler_3.6.2 tools_3.6.2
## [5] digest_0.6.23 evaluate_0.14 lifecycle_0.1.0 tibble_2.1.3
## [9] gtable_0.3.0 pkgconfig_2.0.3 rlang_0.4.4 yaml_2.2.1
## [13] xfun_0.12 gridExtra_2.3 withr_2.1.2 dplyr_0.8.4
## [17] stringr_1.4.0 knitr_1.28 grid_3.6.2 tidyselect_1.0.0
## [21] cowplot_1.0.0 glue_1.3.1 R6_2.4.1 rmarkdown_2.1
## [25] purrr_0.3.3 farver_2.0.3 scales_1.1.0 htmltools_0.4.0
## [29] assertthat_0.2.1 colorspace_1.4-1 ggsignif_0.6.0 labeling_0.3
## [33] stringi_1.4.5 lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4